How AI agents accelerated website delivery and scaled experimentation

A growing community platform needed to unlock faster launches without increasing engineering headcount.

About

This tech innovator offers an all-in-one platform that enables subject matter experts and thought leaders to host and monetize courses, lessons, and live events. By combining community engagement with premium learning experiences, the platform enables participants to teach, inspire, and connect with audiences at scale.

360 Merged PRs

Nearly a 190% increase over the same two-month period in 2024, excluding PM contributions.

Faster

deployment cycles while stabilizing and improving code quality

Scalable,

repeatable AI-enabled delivery model adopted beyond marketing

Challenge

The company’s marketing website is its primary growth lever, but traditional development workflows made iteration slow, costly, and constrained by limited engineering capacity. As growth demands increased, the team needed to ship faster, experiment continuously, and evolve the site without adding headcount or sacrificing quality.

At the same time, the organization was exploring how AI could improve software delivery more broadly—not just for marketing. The challenge wasn’t simply redesigning a website, but rethinking how digital experiences could be built, tested, and scaled across product and marketing.

Approach

We partnered with the client as part of a company-wide agentic coding initiative that began in the core product and expanded to the marketing website. Product and marketing teams adapted their codebases to be more AI-agent friendly, aligning structure, documentation, and conventions so AI tools could operate effectively and safely.

With a smaller, less complex codebase, the marketing site became an ideal environment to operationalize these workflows, despite having fewer engineers. Using tools such as Claude, Codex, Conductor, Cursor, and Devin AI, the team embedded AI agents directly into development.

AI agents generated and refined production-ready code from design documentation and existing components. Parallelized environments allowed multiple agents and developers to work simultaneously across experiments and site versions. Engineers reviewed outputs, validated quality, and made architectural decisions, while AI handled much of the repetitive and exploratory work.

Solution

The redesigned marketing website launched as a controlled A/B experiment, splitting traffic evenly between the legacy and new experiences. More importantly, the project established a repeatable, AI-assisted delivery model that could be reused across teams.

Rather than replacing engineers, AI tools acted as force multipliers—automating tasks like code exploration, bug fixes, and scaffolding—so engineers could focus on system design, performance, and maintainability. The same patterns and tooling were shared with product teams, creating consistency across experiments, onboarding flows, and new feature development.

This approach enabled faster iteration, safer experimentation, and broader collaboration without increasing engineering overhead.

The role of engineering judgment

AI significantly accelerated development, but engineering judgment remained essential. Senior engineers guided AI usage, evaluated code quality, and made architectural trade-offs AI could not reliably handle. Context, nuance, and system-level thinking ensured speed did not come at the expense of reliability.

As one developer noted, better engineers consistently achieve better outcomes with AI. The tools amplified expertise—they did not replace it. This helped the team avoid “vibe coding” and maintain production-grade standards as velocity increased.